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Prediction and classification in nonlinear data analysis: Something old, something new, something borrowed, something blue

Authors
  • Meulman, Jacqueline J.1
  • 1 Leiden University, Data Theory Group, Department of Education, Leiden, 2300 RB, The Netherlands , Leiden
Type
Published Article
Journal
Psychometrika
Publisher
Springer-Verlag
Publication Date
Dec 01, 2003
Volume
68
Issue
4
Pages
493–517
Identifiers
DOI: 10.1007/BF02295607
Source
Springer Nature
Keywords
License
Yellow

Abstract

Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes.

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